code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase_ = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.pro... | 217 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
exc... | 25 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'facebook/convnextv2-tiny-1k-... | 300 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = ... | 25 | 0 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : List[str] = None , _SCREAMING_SNAKE_CASE : List[str] = None ) -> Tuple:
"""simple docstring"""
if start is N... | 433 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset... | 25 | 0 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( lowerCAmelCase_ ) -> List[Any]:
_snake_case = 2
_snake_case = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a )
if n > 1:... | 103 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 25 | 0 |
def UpperCAmelCase ( a_ , a_ , a_ ) -> Dict:
"""simple docstring"""
def update_area_of_max_square(a_ , a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__A = update_area_of_max_square(_a , co... | 55 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytessera... | 25 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCamelCase : Optional[int] = ... | 599 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number >... | 25 | 0 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import ... | 449 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 25 | 0 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase_ ( __lowercase : Tuple ) -> Union[str, Any]:
'''simple docstring'''
if number != int(_a ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise V... | 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sen... | 25 | 0 |
import math
def __UpperCAmelCase ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[Any] = 0 , lowerCamelCase_ : int = 0 ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = end or len(_a )
for... | 105 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simpl... | 25 | 0 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[int] = logging.get_logger(__name__)
... | 527 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
i... | 25 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chi... | 25 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_... | 217 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__... | 25 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
SC... | 300 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Pa... | 25 | 0 |
'''simple docstring'''
import argparse
import datetime
def _snake_case ( _SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": ... | 433 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Op... | 25 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip... | 103 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise Value... | 25 | 0 |
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
__A = [0] * len(_a )
for i in range(1 , len(_a ) ):
# use last results for better performance - dynamic programming
__A = prefix_result[i - 1]
while j > 0... | 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVis... | 25 | 0 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_UpperCamelCase : Union[str, Any] = TypeVar("KEY")
_UpperCamelCase : str = TypeVar("VAL")
@dataclass(frozen=__A , slots=__A... | 599 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
... | 25 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
... | 449 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a... | 25 | 0 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class A_ ( unittest.TestCase ):
def lowercase ( self : Tuple ):
_UpperCAmelCase = i... | 236 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of s... | 25 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird i... | 105 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _Uppe... | 25 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
lowerCAmelCase_ : List[str] = list[list[float | int]]
def UpperCAmelCase ( A : List[Any] , A : List[str] ):
SCREAMING_SNAKE_CASE : int ... | 527 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTe... | 25 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __A ):
"""simple docstring"""
def __init__( self : List[str... | 651 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def l... | 25 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def snake_case( ... | 217 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
exc... | 25 | 0 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 300 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = ... | 25 | 0 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
if len(_a ) == 0:
return False
lowerCAmelCase = len(_a ... | 433 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset... | 25 | 0 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
... | 103 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 25 | 0 |
def UpperCAmelCase ( a_ = 1_0**1_2 ) -> List[str]:
"""simple docstring"""
__A = 1
__A = 0
__A = 1
__A = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerat... | 55 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytessera... | 25 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_c... | 599 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number >... | 25 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
A = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked conte... | 449 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 25 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : Union[str, Any] ) -> int:
'''simple docstring'''
if len(_a ) == 0:
return []
_UpperCAmelCase = min(_a ), max(_a )
_UpperCAmelCase = int(max_va... | 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sen... | 25 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 105 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simpl... | 25 | 0 |
'''simple docstring'''
from math import ceil, sqrt
def UpperCAmelCase ( A : Tuple = 1000000 ):
SCREAMING_SNAKE_CASE : List[Any] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
... | 527 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
i... | 25 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDep... | 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chi... | 25 | 0 |
def snake_case( __magic_name__ ) -> List[str]:
'''simple docstring'''
if len(_a ) <= 1:
return lst
lowercase : List[str] = 1
while i < len(_a ):
if lst[i - 1] <= lst[i]:
i += 1
... | 217 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__... | 25 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a ( unittest.TestCase ):
def ... | 300 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Pa... | 25 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
U... | 433 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Op... | 25 | 0 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.mode... | 103 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise Value... | 25 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :List[Any] = argparse.ArgumentParser()
parser.add_argument('--dump_path', default... | 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVis... | 25 | 0 |
"""simple docstring"""
from __future__ import annotations
class UpperCAmelCase_ :
def __init__( self , a ) -> None:
lowercase__ : Dict = order
# a_{0} ... a_{k}
lowercase__ : List[Any] = [1.0] + [0.... | 599 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
... | 25 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
... | 449 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a... | 25 | 0 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _a ( ) -> tuple[list[int], int]:
"""simple docstring"""
__snake_case : str ... | 26 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __lowercase ):
def lowercase__ ( self : Any ) -> str:
"""simple docstring"""
return [
... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
"configuration_... | 26 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from... | 26 | 1 |
'''simple docstring'''
import math
import sys
def _a ( _lowerCamelCase ) -> str:
"""simple docstring"""
__snake_case : List[str] = """"""
try:
with open(_lowerCamelCase , """rb""" ) as binary_file:
... | 26 |
'''simple docstring'''
from __future__ import annotations
__UpperCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCas... | 26 | 1 |
'''simple docstring'''
import os
import sys
__UpperCamelCase = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAns... | 26 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""only integers accepted as input""" )
else:
__snake_case : List[Any] ... | 26 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
... | 26 |
'''simple docstring'''
from __future__ import annotations
import math
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
"""simple docstring"""
if depth < 0:
raise V... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _A :
def __init__( self : str , __magic_name__ : int , __magic_name__ : int , __magic_name__ : float = 0 ) -> None:
"""simple d... | 26 |
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None ) -> None:
"""simple docstring"""
if start is None:
__snake_case : Optional[Any] ... | 26 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class _A ( __lowercase ):
def __init__( self : int , *__magic_name__ ... | 26 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__UpperCamelCase = logging.getLogger()
... | 26 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from .... | 26 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
... | 26 | 1 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def _a ( _lowerCamelCase ) -> bool:
"""simple docstring"""
_... | 26 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__UpperCamelCase = HUGGINGFACE_HUB_CACHE
__UpperCamelCase = "config.json"
__UpperCamelCase = "diffusion_pytorch_model.bin"
__UpperCamelCase ... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {"configuration_fnet": ["FNET_PRETR... | 26 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
Mobile... | 26 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HP... | 26 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__UpperCamelCase = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is... | 26 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses imp... | 26 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass... | 26 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_visio... | 26 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__UpperCamelC... | 26 | 1 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputE... | 26 |
'''simple docstring'''
def _a ( _lowerCamelCase = 100 ) -> int:
"""simple docstring"""
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : List[Any] = (n * (n + 1) / 2) ** 2
return int(s... | 26 | 1 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanv... | 26 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _A :
def __init__( self : str , __magic_name__ : int , __magic_name__ : int , __magic_name__ : float = 0 ) -> None:
"""simple d... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 26 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
d... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__UpperCamelCase = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"s... | 26 |
'''simple docstring'''
import cva
import numpy as np
class _A :
def __init__( self : Any , __magic_name__ : float , __magic_name__ : int ) -> Optional[int]:
"""simple docstring"""
if k in (0.04, 0.06):
__snake_c... | 26 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteSched... | 26 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( __lowercase ):
lowercase__: Any = ['''image_processor''', '''tokenizer''']
lowercase__: Any = ''... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {"configuration_reformer": ["REFORM... | 26 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 26 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
... | 26 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class _A ( __lowercase ):
def __init__( self : int , *__magic_name__ ... | 26 | 1 |
'''simple docstring'''
import math
__UpperCamelCase = 10
__UpperCamelCase = 7
__UpperCamelCase = BALLS_PER_COLOUR * NUM_COLOURS
def _a ( _lowerCamelCase = 20 ) -> str:
"""simple docstring"""
__snake_case : ... | 26 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase ... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudio... | 26 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__UpperCamelCase = "examples/"
__UpperCamelCase = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init... | 26 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from da... | 26 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __lowercase ):
def lowercase__ ( self : Any ) -> str:
"""simple docstring"""
return [
... | 26 | 1 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__UpperCamelCase = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
... | 26 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from... | 26 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _A ( _... | 26 |
'''simple docstring'''
from __future__ import annotations
__UpperCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCas... | 26 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentPa... | 26 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""only integers accepted as input""" )
else:
__snake_case : List[Any] ... | 26 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__UpperCamelCase = 4
__UpperCamelCase = 3
... | 26 |
'''simple docstring'''
from __future__ import annotations
import math
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
"""simple docstring"""
if depth < 0:
raise V... | 26 | 1 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ... | 26 |
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None ) -> None:
"""simple docstring"""
if start is None:
__snake_case : Optional[Any] ... | 26 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from .... | 26 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__UpperCamelCase = logging.getLogger()
... | 26 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _A ( __lowercase ):
lowercase__: Any = (EulerDiscreteScheduler,)
lowercase__: ... | 26 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
... | 26 | 1 |
'''simple docstring'''
__UpperCamelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformer... | 26 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__UpperCamelCase = HUGGINGFACE_HUB_CACHE
__UpperCamelCase = "config.json"
__UpperCamelCase = "diffusion_pytorch_model.bin"
__UpperCamelCase ... | 26 | 1 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the datas... | 26 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
Mobile... | 26 | 1 |
'''simple docstring'''
__UpperCamelCase = 0 # The first color of the flag.
__UpperCamelCase = 1 # The second color of the flag.
__UpperCamelCase = 2 # The third color of the flag.
__UpperCamelCase = (red, white, blue)
def _a ( ... | 26 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__UpperCamelCase = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _A :
def __init__( self : Optional[Any] , __magic_name__ : int ) -> None:
"""simple docstring"""
__snake_case : List[Any... | 26 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Table... | 26 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__UpperCamelC... | 26 | 1 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> bool:
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
__snake_case : Dict = F'''Input value of [number={number}] must be an integer'''
... | 26 |
'''simple docstring'''
def _a ( _lowerCamelCase = 100 ) -> int:
"""simple docstring"""
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : List[Any] = (n * (n + 1) / 2) ** 2
return int(s... | 26 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __lowercase ):
def lowercase__ ( self : Any ) -> str:
"""simple docstring"""
return [
... | 26 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _A :
def __init__( self : str , __magic_name__ : int , __magic_name__ : int , __magic_name__ : float = 0 ) -> None:
"""simple d... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependenc... | 26 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
d... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
class _A :
def __init__( self : Optional[int] , __magic_name__ : list[list[int]] ) -> str:
"""simple docstring"""
__snake_case : str = TypeError(
... | 26 |
'''simple docstring'''
import cva
import numpy as np
class _A :
def __init__( self : Any , __magic_name__ : float , __magic_name__ : int ) -> Optional[int]:
"""simple docstring"""
if k in (0.04, 0.06):
__snake_c... | 26 | 1 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> Any:
"""simple docstring"""
__snake_case : List[Any] = [0] * len(_lowerCamelCase )
__snake_case : List[Any] = []
__snake_case : List[Any] ... | 26 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( __lowercase ):
lowercase__: Any = ['''image_processor''', '''tokenizer''']
lowercase__: Any = ''... | 26 | 1 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
__snake_case : Any = len(_lowerCamelCase )
__snake_case : Dict = len(matrix[0] )
__snake_case : Tuple ... | 26 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 26 | 1 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModul... | 26 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class _A ( __lowercase ):
def __init__( self : int , *__magic_name__ ... | 26 | 1 |
'''simple docstring'''
def _a ( _lowerCamelCase , _lowerCamelCase ) -> list[int]:
"""simple docstring"""
__snake_case : Tuple = int(_lowerCamelCase )
# Initialize Result
__snake_case : Union[str, Any] ... | 26 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase ... | 26 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 26 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__UpperCamelCase = "examples/"
__UpperCamelCase = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init... | 26 | 1 |
'''simple docstring'''
import torch
def _a ( ) -> Optional[int]:
"""simple docstring"""
if torch.cuda.is_available():
__snake_case : Optional[int] = torch.cuda.device_count()
else:
__snake_case : ... | 26 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __lowercase ):
def lowercase__ ( self : Any ) -> str:
"""simple docstring"""
return [
... | 26 | 1 |
'''simple docstring'''
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes a... | 26 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from... | 26 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = ... | 26 |
'''simple docstring'''
from __future__ import annotations
__UpperCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCas... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int | float:
"""simple docstring"""
if len(_lowerCamelCase ) == 0:
raise ValueError("""find_max() arg is a... | 26 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""only integers accepted as input""" )
else:
__snake_case : List[Any] ... | 26 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class _A ( __lowercase ):
def __init__( self : str , *__magic_name__ :... | 26 |
'''simple docstring'''
from __future__ import annotations
import math
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
"""simple docstring"""
if depth < 0:
raise V... | 26 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__UpperCamelC... | 26 |
'''simple docstring'''
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None ) -> None:
"""simple docstring"""
if start is None:
__snake_case : Optional[Any] ... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _A :
def __init__( self : List[Any] , __magic_name__ : Collection[float] | None = None ) ->... | 26 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__UpperCamelCase = logging.getLogger()
... | 26 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils i... | 26 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
... | 26 | 1 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__UpperCamelCase = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is... | 26 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__UpperCamelCase = HUGGINGFACE_HUB_CACHE
__UpperCamelCase = "config.json"
__UpperCamelCase = "diffusion_pytorch_model.bin"
__UpperCamelCase ... | 26 | 1 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_sch... | 26 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
Mobile... | 26 | 1 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import ja... | 26 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__UpperCamelCase = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is... | 26 | 1 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({... | 26 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass... | 26 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pip... | 26 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__UpperCamelC... | 26 | 1 |
'''simple docstring'''
from torch import nn
def _a ( _lowerCamelCase ) -> Tuple:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
... | 26 |
'''simple docstring'''
def _a ( _lowerCamelCase = 100 ) -> int:
"""simple docstring"""
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : List[Any] = (n * (n + 1) / 2) ** 2
return int(s... | 26 | 1 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order... | 26 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _A :
def __init__( self : str , __magic_name__ : int , __magic_name__ : int , __magic_name__ : float = 0 ) -> None:
"""simple d... | 26 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import Reg... | 26 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
d... | 26 | 1 |
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